diff --git a/internal/ceres/schur_eliminator_test.cc b/internal/ceres/schur_eliminator_test.cc
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+++ b/internal/ceres/schur_eliminator_test.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/schur_eliminator.h"
+
+#include <glog/logging.h>
+#include "ceres/file.h"
+#include "gtest/gtest.h"
+#include "Eigen/Dense"
+#include "ceres/block_random_access_dense_matrix.h"
+#include "ceres/block_sparse_matrix.h"
+#include "ceres/casts.h"
+#include "ceres/detect_structure.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/types.h"
+
+// TODO(sameeragarwal): Reduce the size of these tests and redo the
+// parameterization to be more efficient.
+
+DECLARE_string(test_srcdir);
+
+namespace ceres {
+namespace internal {
+
+class SchurEliminatorTest : public ::testing::Test {
+ protected:
+  void SetUpFromId(int id) {
+    scoped_ptr<LinearLeastSquaresProblem>
+        problem(CreateLinearLeastSquaresProblemFromId(id));
+    CHECK_NOTNULL(problem.get());
+    SetupHelper(problem.get());
+  }
+
+  void SetUpFromFilename(const string& filename) {
+    scoped_ptr<LinearLeastSquaresProblem>
+        problem(CreateLinearLeastSquaresProblemFromFile(filename));
+    CHECK_NOTNULL(problem.get());
+    SetupHelper(problem.get());
+  }
+
+  void SetupHelper(LinearLeastSquaresProblem* problem) {
+    A.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
+    b.reset(problem->b.release());
+    D.reset(problem->D.release());
+
+    num_eliminate_blocks = problem->num_eliminate_blocks;
+    num_eliminate_cols = 0;
+    const CompressedRowBlockStructure* bs = A->block_structure();
+
+    for (int i = 0; i < num_eliminate_blocks; ++i) {
+      num_eliminate_cols += bs->cols[i].size;
+    }
+  }
+
+  // Compute the golden values for the reduced linear system and the
+  // solution to the linear least squares problem using dense linear
+  // algebra.
+  void ComputeReferenceSolution(const Vector& D) {
+    Matrix J;
+    A->ToDenseMatrix(&J);
+    VectorRef f(b.get(), J.rows());
+
+    Matrix H  =  (D.cwiseProduct(D)).asDiagonal();
+    H.noalias() += J.transpose() * J;
+
+    const Vector g = J.transpose() * f;
+    const int schur_size = J.cols() - num_eliminate_cols;
+
+    lhs_expected.resize(schur_size, schur_size);
+    lhs_expected.setZero();
+
+    rhs_expected.resize(schur_size);
+    rhs_expected.setZero();
+
+    sol_expected.resize(J.cols());
+    sol_expected.setZero();
+
+    Matrix P = H.block(0, 0, num_eliminate_cols, num_eliminate_cols);
+    Matrix Q = H.block(0,
+                       num_eliminate_cols,
+                       num_eliminate_cols,
+                       schur_size);
+    Matrix R = H.block(num_eliminate_cols,
+                       num_eliminate_cols,
+                       schur_size,
+                       schur_size);
+    int row = 0;
+    const CompressedRowBlockStructure* bs = A->block_structure();
+    for (int i = 0; i < num_eliminate_blocks; ++i) {
+      const int block_size =  bs->cols[i].size;
+      P.block(row, row,  block_size, block_size) =
+          P
+          .block(row, row,  block_size, block_size)
+          .ldlt()
+          .solve(Matrix::Identity(block_size, block_size));
+      row += block_size;
+    }
+
+    lhs_expected
+        .triangularView<Eigen::Upper>() = R - Q.transpose() * P * Q;
+    rhs_expected =
+        g.tail(schur_size) - Q.transpose() * P * g.head(num_eliminate_cols);
+    sol_expected = H.ldlt().solve(g);
+  }
+
+  void EliminateSolveAndCompare(const VectorRef& diagonal,
+                                bool use_static_structure,
+                                const double relative_tolerance) {
+    const CompressedRowBlockStructure* bs = A->block_structure();
+    const int num_col_blocks = bs->cols.size();
+    vector<int> blocks(num_col_blocks - num_eliminate_blocks, 0);
+    for (int i = num_eliminate_blocks; i < num_col_blocks; ++i) {
+      blocks[i - num_eliminate_blocks] = bs->cols[i].size;
+    }
+
+    BlockRandomAccessDenseMatrix lhs(blocks);
+
+    const int num_cols = A->num_cols();
+    const int schur_size = lhs.num_rows();
+
+    Vector rhs(schur_size);
+
+    LinearSolver::Options options;
+    options.num_eliminate_blocks = num_eliminate_blocks;
+    if (use_static_structure) {
+      DetectStructure(*bs,
+                      options.num_eliminate_blocks,
+                      &options.row_block_size,
+                      &options.e_block_size,
+                      &options.f_block_size);
+    }
+
+    scoped_ptr<SchurEliminatorBase> eliminator;
+    eliminator.reset(SchurEliminatorBase::Create(options));
+    eliminator->Init(num_eliminate_blocks, A->block_structure());
+    eliminator->Eliminate(A.get(), b.get(), diagonal.data(), &lhs, rhs.data());
+
+    MatrixRef lhs_ref(lhs.mutable_values(), lhs.num_rows(), lhs.num_cols());
+    Vector reduced_sol  =
+        lhs_ref
+        .selfadjointView<Eigen::Upper>()
+        .ldlt()
+        .solve(rhs);
+
+    // Solution to the linear least squares problem.
+    Vector sol(num_cols);
+    sol.setZero();
+    sol.tail(schur_size) = reduced_sol;
+    eliminator->BackSubstitute(A.get(),
+                               b.get(),
+                               diagonal.data(),
+                               reduced_sol.data(),
+                               sol.data());
+
+    Matrix delta = (lhs_ref - lhs_expected).selfadjointView<Eigen::Upper>();
+    double diff = delta.norm();
+    EXPECT_NEAR(diff / lhs_expected.norm(), 0.0, relative_tolerance);
+    EXPECT_NEAR((rhs - rhs_expected).norm() / rhs_expected.norm(), 0.0,
+                relative_tolerance);
+    EXPECT_NEAR((sol - sol_expected).norm() / sol_expected.norm(), 0.0,
+                relative_tolerance);
+  }
+
+  scoped_ptr<BlockSparseMatrix> A;
+  scoped_array<double> b;
+  scoped_array<double> D;
+  int num_eliminate_blocks;
+  int num_eliminate_cols;
+
+  Matrix lhs_expected;
+  Vector rhs_expected;
+  Vector sol_expected;
+};
+
+TEST_F(SchurEliminatorTest, ScalarProblem) {
+  SetUpFromId(2);
+  Vector zero(A->num_cols());
+  zero.setZero();
+
+  ComputeReferenceSolution(VectorRef(zero.data(), A->num_cols()));
+  EliminateSolveAndCompare(VectorRef(zero.data(), A->num_cols()), true, 1e-14);
+  EliminateSolveAndCompare(VectorRef(zero.data(), A->num_cols()), false, 1e-14);
+
+  ComputeReferenceSolution(VectorRef(D.get(), A->num_cols()));
+  EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), true, 1e-14);
+  EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), false, 1e-14);
+}
+
+#ifndef CERES_DONT_HAVE_PROTOCOL_BUFFERS
+TEST_F(SchurEliminatorTest, BlockProblem) {
+  const string input_file =
+      JoinPath(FLAGS_test_srcdir,
+                     "problem-6-1384-000.lsqp");  // NOLINT
+
+  SetUpFromFilename(input_file);
+  ComputeReferenceSolution(VectorRef(D.get(), A->num_cols()));
+  EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), true, 1e-10);
+  EliminateSolveAndCompare(VectorRef(D.get(), A->num_cols()), false, 1e-10);
+}
+#endif  // CERES_DONT_HAVE_PROTOCOL_BUFFERS
+
+}  // namespace internal
+}  // namespace ceres